Assessing building performance in residential buildings using BIM and sensor data. Issue 1 (24th September 2019)

• Record Type: Journal Article
• Authors: Rogage, Kay; Clear, Adrian; Alwan, Zaid; Lawrence, Tom; Kelly, Graham
• Is Part Of: International journal of building pathology and adaptation. Volume 38:Issue 1(2020)
• Page Start: 176
• Page End: 191
• Publication Date: 2019-09-24
• Subjects: Smart buildings -- Building performance -- BIM for facilities Management -- Sensor data; Real property surveys -- Periodicals; Buildings -- Environmental engineering -- Periodicals; 624.105
• DOI: 10.1108/IJBPA-01-2019-0012
• Languages: English
• ISSNs: 2398-4708

Abstract

Abstract : Purpose: Buildings and their use is a complex process from design to occupation. Buildings produce huge volumes of data such as building information modelling (BIM), sensor (e.g. from building management systems), occupant and building maintenance data. These data can be spread across multiple disconnected systems in numerous formats, making their combined analysis difficult. The purpose of this paper is to bring these sources of data together, to provide a more complete account of a building and, consequently, a more comprehensive basis for understanding and managing its performance. Design/methodology/approach: Building data from a sample of newly constructed housing units were analysed, several properties were identified for the study and sensors deployed. A sensor agnostic platform for visualising real-time building performance data was developed. Findings: Data sources from both sensor data and qualitative questionnaire were analysed and a matrix of elements affecting building performance in areas such as energy use, comfort use, integration with technology was presented. In addition, a prototype sensor visualisation platform was designed to connect in-use performance data to BIM. Originality/value: This work presents initial findings from a post occupancy evaluation utilising sensor data. The work attempts to address the issues of BIM in-use scenarios for housing sector. A prototype was developed which can be fully developed and replicated to wider housing projects. The findings can better address how indoor thermal comfort parameters can be used to improve housing stock and even address elements such as machine learning for better buildings.